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Fault Detection in Digraphs via Local Diagnosis Structures in Comparison Model.

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Title: Fault Detection in Digraphs via Local Diagnosis Structures in Comparison Model.
Authors: ZHANG, XIAWEI1, LV, YALI2, CHENG, EDDIE3, LIPTÁK, LÁSZLÓ3, LIN, CHENG-KUAN4 cklin@nycu.edu.tw
Source: Journal of Information Science & Engineering. May2026, Vol. 42 Issue 3, p599-614. 16p.
Subjects: Directed graphs, Fault diagnosis, Algorithms, Simulation methods & models, Hypercube networks (Computer networks)
Abstract: Local diagnosability is an effective approach to assess system diagnosability by evaluating each processor individually. According to the basic definition of diagnosis, the underlying topology of existing local comparison diagnosis models can be modeled as directed graphs with bidirectional edges, so it is difficult to apply them in classical digraphs. In this paper, we introduce a (p + q)-directed local diagnosis structure D(u, p, q) and present a local diagnosis algorithm for digraphs. This algorithm identifies faults or fault-free status of each processor under the comparison model. Our results indicate that any vertex in a digraph with the structure D (u, p,q) is locally (p + q)-diagnosable, with a time complexity of O(p + q). We apply the (p + q)-directed local diagnosis structure to unidirectional hypercubes, determining the local diagnosability for any vertex. Simulation results show that our algorithm maintains high performance even with a 40% fault probability for each vertex, achieving ACC ≥ 0.836, PPV ≥ 0.827, and NPV ≥ 0.816. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Directed+graphs%22">Directed graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Hypercube+networks+%28Computer+networks%29%22">Hypercube networks (Computer networks)</searchLink>
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  Data: Local diagnosability is an effective approach to assess system diagnosability by evaluating each processor individually. According to the basic definition of diagnosis, the underlying topology of existing local comparison diagnosis models can be modeled as directed graphs with bidirectional edges, so it is difficult to apply them in classical digraphs. In this paper, we introduce a (p + q)-directed local diagnosis structure D(u, p, q) and present a local diagnosis algorithm for digraphs. This algorithm identifies faults or fault-free status of each processor under the comparison model. Our results indicate that any vertex in a digraph with the structure D (u, p,q) is locally (p + q)-diagnosable, with a time complexity of O(p + q). We apply the (p + q)-directed local diagnosis structure to unidirectional hypercubes, determining the local diagnosability for any vertex. Simulation results show that our algorithm maintains high performance even with a 40% fault probability for each vertex, achieving ACC ≥ 0.836, PPV ≥ 0.827, and NPV ≥ 0.816. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.6688/JISE.202605_42(3).0007
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      – Code: eng
        Text: English
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      – SubjectFull: Directed graphs
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Simulation methods & models
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      – SubjectFull: Hypercube networks (Computer networks)
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      – TitleFull: Fault Detection in Digraphs via Local Diagnosis Structures in Comparison Model.
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              Text: May2026
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              Y: 2026
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